(1) A decision tree method has been developed to distinguish the internal pixels of an object with a given chain encoded boundary, regardless of the intensities of these pixels are higher or lower than that of background. (2) An objective boundary tracking method has been created to select one out of several isodensity contours. The criterion for selection is based on an optimizing objective function of area, shape and integrated density. (3) Several measures of visual texture, based on the average local intensity, have been generated. Comparison between this texture and the texture based on transition probability matrix has been studied on bladder tissue sections. (4) A concavity finding algorithm for the chain encoded boundary has directly been produced. (5) These algorithms have been applied to the images of bladder cancer nuclei, white blood cells and computerized tomography.